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Record W4288096307 · doi:10.2196/38616

Developing a Health Care Transition Intervention With Young People With Spinal Cord Injuries: Co-design Approach

2022· article· en· W4288096307 on OpenAlexvenueno aff
Emily Alice Bray, Bronwyn Everett, Ajesh George, Yenna Salamonson, Lucie M. Ramjan

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)Focus groupQualitative researchNursingHealth careActive listeningPsychologyMedicineMedical educationPsychotherapistBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Successful transition from pediatric to adult health care settings supports long-term health management and better overall outcomes in all domains. However, young people with spinal cord injuries (SCIs) continue to report challenges and unmet needs during the transition process. Including end users in health care research and intervention design is paramount as interventions designed in this way better meet their specific needs and are often more innovative. Although studies have reported involving young people with chronic conditions in the development of health care transition (HCT) interventions, few details have been provided as to how this was achieved. OBJECTIVE: This study outlined the co-design and development of an HCT intervention to support young people with SCIs. It contextualized the co-design process, methods, materials used, and steps implemented from defining the problem to conceiving and designing the solution. This was accomplished by understanding and listening to end users' needs and recommendations for HCT. METHODS: Using participatory methods, this qualitative study reports the co-design of an HCT intervention to support young people with SCIs and parents or caregivers. Two co-design workshops were conducted: one with young people with SCIs and one with parents and caregivers. Categories were defined through a hybrid deductive and inductive qualitative content analysis process that was informed by the Care Transitions Framework and guided the development of the HCT intervention. Following the creation of a prototype intervention, young people with SCIs, parents and caregivers, and key pediatric SCI stakeholders provided feedback on the intervention content and design in focus groups. Similar to the workshops, the focus groups were analyzed using a hybrid deductive and inductive qualitative content analysis process informed by the Care Transitions Framework. The Enhancing the Quality and Transparency of Health Research guidelines for qualitative research (Consolidated Criteria for Reporting Qualitative Research) were applied. RESULTS: Overall, 4 young people and 4 parents or caregivers participated in the co-design workshops. Key recommendations for the HCT intervention were that participants wanted a "one-stop shop" for all their transition information needs and an editable portable medical summary to take with them to appointments. On the basis of the analysis of participants' recommendations from the workshops, it was determined that a website would be an appropriate hosting platform for the interventions. The focus group feedback on the design and content of the prototype website was extremely positive, with minor recommendations for improvement. CONCLUSIONS: This is the first study to co-design and develop an HCT intervention in partnership with young people with SCIs and parents and caregivers. Although the study sample was small, it has shown that it is possible to meaningfully engage and empower young people with SCIs and parents and caregivers in the co-design of an HCT intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-053212.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.052
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0050.005
Scholarly communication0.0050.004
Open science0.0030.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.191
GPT teacher head0.533
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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